With the recent advances in device and service personalization and recommendation, implicit user identification technology is getting more popular. Instead of asking a user to enter a user name and a password, which makes the user experience cumbersome, technologies have been developed to implicitly identify the user. For example, some technologies use GPS and accelerometers built inside mobile devices to analyze user behavior and operation patterns on the device in order to discover the current user. For TVs, solutions are developed based on computer vision for face detection and recognition technique to discover user identity. However, it may be difficult for ordinary users to allow a video camera staring at them during the supposedly relaxing TV watching time. Also, there may be privacy concerns when those TVs are placed in bedrooms.
Further, some technologies try to use remote control to send certain user information to TVs. However, such existing technologies often use simple techniques and in general do not consider the user's reaction to TV contents. Such approach may overlook the fact that the human being's mood can be significantly affected by the content he/she is watching and the mood has impact on the user behavior as well. Further, when the traditional TVs are eventually replaced by smart TVs, the user's reaction to a smart TV and the recommendations from the smart TV often contain plenty implicit but valuable feedback which tells who the user is.
The disclosed methods and systems are directed to solve one or more problems set forth above and other problems.